The present invention relates to a system for evaluating hyperdocuments with the use of at least one trained artificial neural network.
Hyperdocuments are retrieved for viewing and/or other use through network connections, file transfers, disk drives or other sources of information. As used in this patent, the term “hyperdocument” means at least one electronically-accessible or readable document, data, image or file, a portion or combination thereof, or a copy thereof. For example, a hyperdocument may be information extractable from a hypertext database or hypermedia system, such as hypertext, images, video, sound, text, or phrases displayed or retrieved as a web page, file, electronic communication or other form of media. A hyperdocument may be a document or web page that includes or points to multiple images, text, video, etc., a compilation of hyperdocument files, a compressed or archived file (e.g. ZIP, TAR, etc.), a document or file that itself is not a hyperdocument (e.g. Microsoft Word documents, Adobe pdf files), but contains one or more hyperdocuments. Often, it is desirable or necessary to evaluate, or review, hyperdocuments for any number of reasons. For example, it may be desirable to evaluate hyperdocuments to determine if they should be provided to one or more users or other destinations for viewing, display, saving, download or another use. For other examples, hyperdocuments are often evaluated for security screening, virus scanning or filtering for, spam or other content. As used in this patent, the term “user” means at least one person, software, hardware, system, designated recipient of a hyperdocument or any other entity or a combination thereof.
Present hyperdocument evaluations systems have various limitations. For example, in the field of hyperdocument filtering, typical existing systems are based upon look-up tables or the like. Some examples of commercially available systems are Norton AntiVirus, Net Nanny and AOL Parental Controls. These systems search the hyperdocument for certain text words or phrases that are compared to text words or phrases maintained in a database. For example, in source comparison systems, the source, address or other identifier of the hyperdocument is typically compared to a list of known sources, addresses or other identifiers. For another example, existing text or html-based language filters must maintain a list of words or phrases to be blocked. Other presently available systems utilize contour transformation, context boundary analysis, templates for categorizing specific hypertext and pixel comparisons for color images. An example of existing image comparison software is ScreenShield by Guardware, Inc., which compares hyperdocument pixel color to a skin tones database.
The ability of typical existing systems to evaluate hyperdocuments is thus limited to the text, phrases, images or other data entered into and maintained in a database. To stay current, the lists must be updated for changing sources or sites, new sources or data, etc. Known systems utilizing contour transformations or boundary analysis are limited to specific criteria previously established for the system.
It should be understood, however, that the above discussion is only exemplary of existing technology and its limitations. The existing technology has other limitations. Further, each embodiment of the present invention and each claim of this patent does not necessarily address or solve the above-described or any particular limitations or disadvantages of existing technology and is otherwise not limited in any way by the above discussion.
Accordingly, there exists a need for a system for evaluating hyperdocuments having one or more of the following attributes, capabilities or features: uses trained artificial neural network technology for hyperdocument evaluation; uses trained artificial neural network technology for evaluation of images; uses trained artificial neural network technology for evaluation of pornographic content in hyperdocuments; transforms electronic image data into a format useful by an artificial neural network for hyperdocument analysis; evaluates and selectively categories hyperdocuments based upon artificial neural network analysis; allows for selective categorizations of content within hyperdocuments; evaluates the accuracy of hyperdocument retrieval; allows hyperdocument retrieval based upon a sliding scale associated with specific criteria; selectively allows retrieval of information from within hyperdocuments; provides hyperdocument retrieval control based upon the categorization of the hyperdocument or content within; allows user-selected criteria for hyperdocument retrieval; provides flexibility in hyperdocument retrieval; categorizes the hyperdocuments, such as images, rather than merely inspecting text or phrases; evaluates the actual hyperdocument content, not merely its address, description or reference; is not limited to a comparison list or database; does not rely upon maintenance of a comparison list or database; does not depend on the selection criteria imposed by the system; includes a timeout function; or any combination thereof.